We quantify the significance and magnitude of the effect of measurement error in satellite weather data on modeling agricultural productivity. To provide rigor to our approach, we combine geospatial weather data from a variety of weather products with the geo-referenced household survey data from six Sub-Saharan African countries that are part of the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. Our goal is to provide systematic evidence on obfuscation methods, satellite data source, and weather metrics in order to determine which of these elements have strong predictive power over a large set of crops and countries and which are only useful in highly specific settings. Collaborators: T. Kilic (World Bank), S. Murray (World Bank), and B. McGreal (UArizona).

We investigate the sources of variability in agricultural production and their relative importance in the context of weather index insurance for smallholder farmers in India. Using parcel-level panel data, multilevel modeling, and Bayesian methods we measure how large a role seasonal variation in weather plays in explaining yield variance. Seasonal variation in weather accounts for 19-20 percent of total variance in crop yields. Motivated by this result, we derive pricing and payout schedules for actuarially fair index insurance. These calculations shed light on the low uptake rates of index insurance and provide direction for designing more suitable index insurance. Collaborators: F. Viens (Michigan State) and G. Shively (Purdue).